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1 CHILDREN AND FAMILIES EDUCATION AND THE ARTS ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INFRASTRUCTURE AND TRANSPORTATION INTERNATIONAL AFFAIRS LAW AND BUSINESS NATIONAL SECURITY POPULATION AND AGING PUBLIC SAFETY SCIENCE AND TECHNOLOGY TERRORISM AND HOMELAND SECURITY The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. This electronic document was made available from as a public service of the RAND Corporation. Skip all front matter: Jump to Page 16 Support RAND Browse Reports & Bookstore Make a charitable contribution For More Information Visit RAND at Explore RAND Health View document details Limited Electronic Distribution Rights This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. This electronic representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of RAND electronic documents to a non-rand website is prohibited. RAND electronic documents are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions.

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5 PREFACE Quality metrics play an increasingly important role in the evaluation and reimbursement of post-acute providers. Currently, it is difficult to ascertain whether changes in inpatient rehabilitation facility (IRF) patient outcomes are due to changes in treatment or the case mix of patients seen in IRFs. To address this issue, the Medicare Payment Advisory Commission (MedPAC) contracted with the RAND Corporation (1) to develop risk-adjusted quality metrics at the provider level for IRFs, (2) to develop methods to address low case volume and uncommon events, and (3) to use those metrics to estimate national trends in IRF quality from 2004 to This report presents the results for five IRF outcomes: (1) functional gain, (2) discharge to the community, (3) 30- day readmission to acute care given discharge to the community, (4) 30-day readmission to skilled nursing facility (SNF) given discharge to the community, and (5) discharge directly to acute care. The risk-adjustment models presented here minimize the potential for selection and can ultimately be used as the basis for public reporting and qualitybased reimbursement. This report should be of interest to researchers involved either in inpatient rehabilitation facilities or the development of IRF quality reports or in outcomes studies that require risk adjustment. The study should also interest funders of IRF services, those working on risk adjustment methods in health services research, and patients (and their families) who have a need for IRF services. iii

13 SUMMARY Quality metrics play an increasingly important role in the evaluation and reimbursement of post-acute providers, and inpatient rehabilitation facilities (IRFs) in particular. The Medicare Payment Advisory Commission (MedPAC) uses aggregate trends in quality measures, such as functional gain, to assess the adequacy of payments to providers. In addition, the Patient Protection and Affordable Care Act (ACA) requires IRFs to publicly report quality data or be penalized in annual payment updates. Eventually, quality data could be used in a value-based purchasing scheme for IRFs as is being developed for other post-acute settings under ACA. In this report, we describe our development of risk-adjusted quality metrics for IRFs, focusing on five patient outcomes: (1) functional gain, (2) discharge to the community, (3) 30-day readmission to acute care, (4) 30-day readmission to a skilled nursing facility (SNF), and (5) discharge directly to acute care. We start by describing the development of our model, including our choice of independent variables, the selection of our model specification, and the identification of a minimum patient volume to support a quality metric. Next, we present results from our risk-adjustment estimation. We identify changes in model parameter estimates across years in the sample period and decompose average patient outcome trends into changes stemming from the case-mix composition of patients seen in IRFs and those due to changes in expected outcomes given the observable case mix. Finally, we examine the persistence of quality estimates and differential trends in quality by IRF provider characteristics. For each outcome, we specified a model that contained individual socioeconomic and demographic characteristics, comorbid condition indicators, Impairment Group Code (IGC) indicators, and age-by-sex interactions. We estimated nine alternative specifications for each model and chose the model with the best fit. In each case, the best model was either the full model or the model that dropped the age-by-sex interactions. Model fit was good, but there remained a substantial amount of unexplained outcome variation by IRF. xi

14 We considered two main modeling approaches for the IRF risk adjustment: (1) random-effects or hierarchical models and (2) fixed-effects models in which each IRF s contribution to quality is determined by a fixed indicator in the model. For each of the outcomes models, we found strong evidence to reject the random-effects assumption, indicating that quality measures based on random-effects estimates would be biased. We therefore adopted the fixed-effects approach. In the case of this application, even though the number of IRFs was large (1,400), the very large data set (2.5 million observations) made it feasible to estimate fixed-effects models. One notable advantage of the fixedeffects approach is that the models generate an empirical representation of the distribution of quality across IRFs. Unlike random-effects models, which assume a distribution for IRF quality, the fixed-effects approach imposes no structure. In addition, by manipulating the models so that each IRF s estimate represents its difference from the average-quality IRF, the quality metrics are relatively easy to manipulate and interpret. Although our study sample contained approximately 2.5 million observations, not every IRF had large patient volume. Thus, we developed criteria for determining if the quality estimate for an IRF was too uncertain or unreliable to merit reporting. Starting with the assumption that the estimated uncertainty (standard error or confidence interval of the quality measure) is the best means for determining the measure s reliability, we examined the relationship between IRF volume and the quality measure s uncertainty, and we examined various criteria that used only IRF volume to determine the omission of IRFs. We found that the use of a volume standard resulted in either the omission of too many IRFs (many of which had reliable estimates of quality) or the inclusion of IRFs that had very uncertain quality estimates. We therefore recommend the use of a combined standard that requires a minimum patient volume (we used n = 30) to eliminate verysmall volume IRFs and a maximum uncertainty level (confidence interval > one standard deviation of the outcome rate across institutions) in order to exclude IRFs for which the quality estimates were not precise. We found that these criteria together resulted in the exclusion of a very small number of IRFs. We examined model stability by comparing model estimates based on data from early in the study period with model estimates based on data from late in the study period. In xii

15 every case but one (30-day readmission to SNF comparing estimates with estimates), we found evidence that the model parameter estimates changed over time. This implies that caution should be used when making model estimates using data pooled over several years. It also suggests that risk-adjustment models will need to be recalibrated regularly. As a result, we estimated year-specific risk-adjustment models to generate year-specific quality indicators for each IRF. These indicators can be compared either within each year, to reveal any IRF s quality relative to other IRFs or to the average IRF, or over time to reveal trends in quality. We also used the models to examine trends in quality during the study period. A comparison of raw and adjusted outcome rates shows the contribution of real quality changes and case-mix changes, revealing that real improvements in quality have been masked by increasing severity in case mix during each year of the study and for each of the outcomes considered. Among the key findings are the following: 1. The raw outcome rates for four of the five quality measures worsened over the study period. Functional Independence Measure (FIM) gain improved by about two points, but discharge to the community (18 percent to 11 percent), 30-day readmission to acute care (10.5 percent to 11.5 percent), 30-day readmission to SNF (3.1 percent to 3.7 percent), and discharge directly to acute care (8.6 percent to percent) all worsened. 2. The declining raw rates were caused by worsening case mix during the period. After adjusting for case mix, we found that quality improved on every metric; in the case of FIM gain, quality improved by more than the improvement in the raw rate. 3. Quality at individual IRFs persisted over the study period. We identified two cohorts of IRFs using 2004 quality estimates: the lowest-quartile performers and the highest-quartile performers. We then examined the average quality of those cohorts in each of the study years, and we found that the 2004 high-performing IRFs continued to be high performers throughout, that the 2004 low performers continued to be low performers, and that the gap in quality did not narrow substantially. xiii

16 Our work indicates that the existing data provide ample information to estimate riskadjustment models for the purpose of examining and reporting IRF quality. Furthermore, our findings show that, because of the substantial worsening of case mix, the use of highquality risk-adjustment models as the basis for quality reporting is essential for revealing overall quality trends in the market. Finally, we find that the risk-adjustment parameter estimates change over time, indicating that the models should be recalibrated regularly and, if feasible, year-specific models should be used. xiv

17 ACKNOWLEDGMENTS We would like to acknowledge the assistance and guidance of our MedPAC project officer, Christine Aguiar, policy analyst. We also thank the RAND quality assurance peer reviewers, John Engberg and David Auerbach, for their very helpful comments on the report. xv

21 1. INTRODUCTION Quality metrics play an increasingly important role in the evaluation and reimbursement of post-acute providers, and inpatient rehabilitation facilities (IRFs) in particular. The Medicare Payment Advisory Commission (MedPAC) uses aggregate trends in quality measures, such as functional gain, to assess the adequacy of payments to providers. In addition, the Patient Protection and Affordable Care Act (ACA) requires IRFs to publicly report quality data or be penalized in annual payment updates. Eventually, quality data could be used in a value-based purchasing scheme for IRFs, as is being developed for other post-acute settings under ACA. In order to distinguish IRFs from acute care hospitals, the Center for Medicare and Medicaid Services (CMS) requires that 60 percent of a facility s caseload be diagnosed with conditions deemed to require intensive inpatient rehabilitation. Starting in 2004, this list of conditions was altered, most notably to exclude many types of joint replacement. The effect of this policy change was a lower volume but higher severity of IRF patients (MedPAC, 2011). The change in composition of IRF patients over time makes the interpretation of quality measures more challenging. For example, MedPAC (2011) shows that functional gain (as measured by Functional Independence Measure (FIM)) has increased between 2004 and However, it is difficult to ascertain whether these improvements in outcomes are due to improved treatment in IRFs or to the fact that the types of patients admitted to IRFs had a greater potential for functional gain, as noted in MedPAC (2011). Risk adjustment has the potential to improve the comparability of quality metrics both across providers and over time. Risk-adjusted quality measures can be used internally by MedPAC to determine payment adequacy and externally for public reporting. Eventually, they could form the basis of a value-based purchasing scheme for IRFs. Such models allow policymakers to distinguish between aggregate changes in quality measures due to treatment patterns and those due to the composition of patients seen in IRFs. In this report, we describe our development of risk-adjusted quality metrics for IRFs, focusing on five patient outcomes: (1) functional gain, (2) discharge to the community, (3) 30-day readmission to acute care, (4) 30-day readmission to a skilled nursing facility (SNF), and (5) 1

22 discharge directly to acute care. We start by describing the development of our model, including our choice of independent variables, the selection of our model specification, and the identification of a minimum patient volume to support a quality metric. Next, we present results from our riskadjustment estimation. We identify changes in model parameter estimates across years in the sample period and decompose average patient outcome trends into changes stemming from the composition of patients seen in IRFs and those due to changes in expected outcomes as a function of observable characteristics. Finally, we examine the persistence of quality estimates and differential trends in quality by IRF provider characteristics. 2

23 2. METHODS Data and Choice of Risk Adjustors Our primary data source is the Medicare Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF PAI) from 2004 to At admission, patients personal and demographic characteristics, admission class, preadmission living status, insurance information, impairment group (i.e., reason for rehabilitation), etiologic diagnosis (i.e., diagnosis leading to impairment group), up to ten comorbid conditions and preexisting complications, functional status, cognitive status, and other information are entered on the IRF-PAI. The impairment requiring rehabilitation, cognitive and functional status at admission, age, and the severity of comorbidities are then used to assign each patient to a case mix group, which determines the amount of the prospective payment a facility will receive (MedPAC, 2010). Cognitive status, functional status, and new complications are also recorded at discharge in order to measure quality of care. The quality outcomes used in our models are derived from the IRF PAI data. The first outcome is improvement in functional status, as measured by the change in the FIM scale between admission and discharge. The FIM scale contains 18 items, 13 of which are physical or motor items and 5 of which are cognitive items. Each item is scored on a seven-point scale, where 1 indicates low function and 7 indicates high function. The total FIM score ranges from 18 to 126; the FIM motor score ranges from 13 to 91. The IRF PAI also indicates whether the patient has been discharged back to an acute care hospital. We augmented IRF PAI data with the Medicare Provider Analysis and Review (MedPAR) acute claims occurring within a year prior to each IRF stay to capture admissions to a SNF or an acute care hospital within 30 days of discharge from the IRF. We recorded all comorbidities in acute claims from the prior year, as well as complications from the preceding acute stay. We also linked IRF patients to the Medicare Denominator File to obtain a more complete set of demographic characteristics. Finally, we linked the provider information from the Medicare Provider of Services File in each year, including freestanding versus hospital-based status, urban and rural location, facility ownership status (i.e., for-profit, government-run, or nonprofit), and bed 3

24 size. We used provider characteristics to examine quality trends separately by provider characteristics, not in the risk adjustment. We selected patient characteristics that are likely to influence post-acute outcomes as risk adjustors for our models. Demographic characteristics include patient age (indicator variables for five-year intervals), race and ethnicity (non-hispanic white, non-hispanic black, Hispanic, and other race), marriage status, dual eligibility (for Medicare and Medicaid), and disability status. We included comorbidities identified by Elixhauser (1998) that are predictive of hospital charges, length of stay, and patient health outcomes. 1 We controlled for complications present at admission from Iezzoni et al. (1994) that are likely to have a continued effect after hospital discharge and could influence post-acute outcomes in particular, dropping transient metabolic derangements and medication side effects. We also included additional complications that may affect post-acute outcomes in a Medicare population. Our final list included postoperative pulmonary compromise; post-operative gastrointestinal hemorrhage; cellulitis or decubitus ulcer; septicemia; pneumonia; mechanical complications due to a device, implant, or graft; shock or arrest in the hospital; postoperative acute myocardial infarction (AMI); postoperative cardiac abnormalities other than AMI; venous thrombosis and pulmonary embolism; procedure-related perforation or laceration; acute renal failure; delirium; and miscellaneous complications. We also strove to capture changes in patient composition attributable to changes in the 75 percent rule implemented starting in Prior to the revision of the 75 percent rule in April 2004, a facility was designated an IRF (and thus exempt from the acute Prospective Payment System) if at least 75 percent of its patients in a facility received treatment for the following conditions: stroke, spinal cord injury, congenital deformity, amputation, major multiple trauma, fracture of femur, brain injury, polyarthritis, neurological disorders, or burns (Carter et al., 2003). However, prior to 2004, the 75 percent rule was not enforced, and polyarthritis was often interpreted to include lower-extremity joint replacement. The new regulations implemented in!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 These comorbidities include congestive heart failure, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, hypertension, paralysis, other neurological disorders, chronic pulmonary disease, diabetes (uncomplicated), diabetes (complicated), hypothyroidism, renal failure, liver disease, peptic ulcer disease excluding bleeding, AIDS, lymphoma, metastatic cancer, rheumatoid arthritis/ collagen vascular diseases, coagulopathy, obesity, weight loss, fluid and electrolytes disorders, blood loss anemia, deficiency anemias, alcohol abuse, drug abuse, psychoses, and depression. 4

25 2004 mandated enforcement of the 75 percent rule, but also replaced the polyarthritis category with a more specific list that includes three arthritis conditions for which appropriate, aggressive, and sustained outpatient therapy has failed, and joint replacement for both knees or hips when surgery immediately precedes admission, when BMI is greater than or equal to 50, or when age is greater than 85 (MedPAC, 2010). Enforcement of the 75 percent rule was originally to be phased in over a five-year period, starting from 50 percent in However, it was decided in 2007 that the compliance threshold would stay at 60 percent (MedPAC, 2010). With insufficiently specific patient controls, differences in severity or composition within patient categories across facilities could be misattributed to facility quality after regulatory changes. Adequately controlling for the impairment requiring rehabilitation is an important dimension of capturing changes in composition. Patients are categorized at admission into one of 85 Impairment Group Codes (IGCs), which are then nested in 21 Rehabilitation Impairment Categories (RICs). Regulatory changes in 2004 map more closely to IGCs than RICs. Lowerextremity joint replacement patients count toward the 75 percent rule if the replacement is bilateral under certain circumstances. Thus, while the overall Replacement of Lower Extremity Joint RIC is too broad to capture this change, the specific IGC codes within this RIC include separate categories for unilateral versus bilateral replacement for hip, knee, and joint. Paired with the Elixhauser obesity control and age controls, the patient characteristics included in our riskadjustment models map to these regulatory changes. The Arthritis IGCs, in contrast, are not extensively disaggregated and cannot be directly mapped to the changes in arthritis categories counting toward the threshold. However, in general, linearly controlling for IGC and the extensive list of patient demographic characteristics, comorbidities, and complications provides an approximate mapping to the regulation changes and captures most changes in patient composition that could bias quality estimates. Specification Tests Our basic model specifies patient outcomes as follows:!!"#!!!!!!!"#!!!"# (1) 5

26 where y is an outcome for patient i in facility p at time t,! is a facility-specific effect, x includes demographic and health characteristics that influence patient outcomes, and u is a person-level error term. Note that all health-related measures that are used for controls must be defined at or prior to admission to the IRF to avoid the possible influence of IRF quality on the measures. The facility-specific effect,!, can be modeled using either random-effects or fixed-effects models. A random-effects specification assumes that the facility-specific effect is distributed independently of patient characteristics (x). Random-effects models estimate model parameters using variation both across and within facilities, leading to more-efficient estimation. However, the assumption of independence of! and x is not necessarily valid. Fixed-effects models make only the assumption that the error term (u) is uncorrelated with patient characteristics (x), and they impose no restrictions on the distribution of quality across IRFs. In addition, the estimated fixed effects themselves form an intuitive quality metric. However, we were unable to estimate fixed-effects models with too few observations per panel member, and models with many panel members are computationally challenging to estimate. Our analyses dataset is very large, mitigating the former issue. Improvements in technology (e.g., dual core processors and statistical software harnessing this computational power) make it feasible to estimate these models. We tested the random effects assumption of independence between the facility-specific effect and patient characteristics using a Hausman test. Under the null hypothesis of independence, the random-effects and fixed-effects parameter estimates for the x variables are both consistent and thus should be equivalent. The Hausman test uses the following test statistic:!!!!"!!!"!!!!!!"!!!"!!!!!!"!!!"! (2)! where!!"!!!" represents the difference between the random-effects (RE) and fixed-effects (FE) estimates of parameter estimates, and V is the variance. H is distributed with a chi-squared distribution; large values of H imply statistically significant differences between the fixed-effects and random-effects estimates and lead us to reject the null hypothesis and, therefore, the randomeffects formulation. We outline the results of Hausman tests in later sections but note here that we found consistent and strong evidence to reject the equivalence of the random-effects and fixed-effects parameter 6

27 estimates. This implies the correlation of provider effects and patient characteristics, and thus we used fixed-effects specifications in our risk-adjustment models. FIM gain is a continuous variable; as such, we estimated linear fixed-effects regressions in the risk-adjustment models. The other outcomes (discharge to community, readmission to acute hospital, readmission to SNF or longterm care, and discharge directly to acute care) are dichotomous equal to one or zero and thus we estimated logistic regressions. A potential drawback of fixed-effects logistic regression models is that each panel member (e.g. IRF) must have successes and failures to be included in the estimation. Thus, these models are sometimes referred to as conditional fixed-effects models. In addition, because the sample selection is conditional on the dependent variable, the fixed-effects estimates may be biased if the number of cases within panel members (IRFs) is small. The very large sample sizes per facility available for our analyses mitigate this concern, but they do create an additional reason to be concerned with quality estimates for institutions that have few cases. For all outcomes, we estimated facility fixed effects directly for use as the quality metric by including indicator variables for each facility. The model is specified such that the reference (or omitted) category is the average risk-adjusted outcome across facilities, not weighting by patient volume. For example, in the case of FIM gain, the quality metric for a given facility can be interpreted as the average FIM gain in the facility relative to the IRF average FIM gain, holding constant patient demographic and health characteristics. The fixed-effects coefficient estimates for the FIM gain model are directly interpretable as the increment in outcome due to the quality of the facilities. For the dichotomous outcomes, the coefficient estimates are difficult to interpret directly. Therefore, rather than using the fixed-effects coefficient estimates directly, we exponentiated them and multiplied them by the overall outcome rate in the sample, so that the quality metrics are directly interpretable in units of the outcome. We evaluated an extensive set of models using varying combinations and functional forms for patient characteristics. To evaluate alternative specifications, we created estimation and validation samples from the 2004 through 2009 sample period, where two-thirds of patients in each facility were randomly assigned to the estimation sample and one-third was assigned to the validation sample. We estimated each specification in the estimation sample, and then examined the fit of the model in the validation sample. For continuous variables (i.e., FIM gain and FIM gain motor), we compared the mean square predicted error using the predicted values in the validation sample 7

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